# Importing Data
CZECK <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Czeck.xlsx")
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
library(readxl)
Czeck <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Czeck.xlsx")
View(Czeck)
# Importing Data
CZECK <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Czeck.xlsx")
# Creating Time Series Data
CZECK_ts <- ts(CZECK, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
CZECK_ts
sum(is.na(CZECK_ts))
library(forecast)
CZECK_ts <- tsclean(CZECK_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(CZECK_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
CZECK_ts_model <- auto.arima(CZECK_ts)
CZECK_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
CZECK_ts_forecast <- forecast (CZECK_ts_model, level=c(95), h=257)
plot(CZECK_ts_forecast)
CZECK_ts_forecast
write.table(CZECK_ts_forecast, file="Czeck_TSA.csv", sep=",")
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Step-3 of the Box-Jenkins Methodology (Diagnosis Checking)
library(tseries)
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=257)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
GERMANY <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Germany.xlsx")
# Creating Time Series Data
GERMANY_ts <- ts(GERMANY, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
GERMANY_ts
sum(is.na(GERMANY_ts))
library(forecast)
GERMANY_ts <- tsclean(GERMANY_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(GERMANY_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
GERMANY_ts_model <- auto.arima(GERMANY_ts)
GERMANY_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
GERMANY_ts_forecast <- forecast (GERMANY_ts_model, level=c(95), h=257)
plot(GERMANY_ts_forecast)
GERMANY_ts_forecast
write.table(GERMANY_ts_forecast, file="Germany_TSA.csv", sep=",")
# Importing Data
ITALY <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Italy.xlsx")
# Creating Time Series Data
ITALY_ts <- ts(ITALY, start=c(2004,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
ITALY_ts
sum(is.na(ITALY_ts))
library(forecast)
ITALY_ts <- tsclean(ITALY_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(ITALY_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
ITALY_ts_model <- auto.arima(ITALY_ts)
ITALY_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
ITALY_ts_forecast <- forecast (ITALY_ts_model, level=c(95), h=258)
plot(ITALY_ts_forecast)
ITALY_ts_forecast
write.table(ITALY_ts_forecast, file="Italy_TSA.csv", sep=",")
# Importing Data
JAPAN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Japan.xlsx")
# Creating Time Series Data
JAPAN_ts <- ts(JAPAN, start=c(2001,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
JAPAN_ts
sum(is.na(JAPAN_ts))
library(forecast)
JAPAN_ts <- tsclean(JAPAN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(JAPAN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
JAPAN_ts_model <- auto.arima(JAPAN_ts)
JAPAN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
JAPAN_ts_forecast <- forecast (JAPAN_ts_model, level=c(95), h=258)
plot(JAPAN_ts_forecast)
JAPAN_ts_forecast
write.table(JAPAN_ts_forecast, file="Japan_TSA.csv", sep=",")
# Importing Data
KOREA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Korea.xlsx")
# Creating Time Series Data
KOREA_ts <- ts(KOREA, start=c(2008,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
KOREA_ts
sum(is.na(KOREA_ts))
library(forecast)
KOREA_ts <- tsclean(KOREA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(KOREA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
KOREA_ts_model <- auto.arima(KOREA_ts)
KOREA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
KOREA_ts_forecast <- forecast (KOREA_ts_model, level=c(95), h=257)
plot(KOREA_ts_forecast)
KOREA_ts_forecast
write.table(KOREA_ts_forecast, file="Korea_TSA.csv", sep=",")
# Importing Data
NEITHERLAND <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Neitherland.xlsx")
# Creating Time Series Data
NEITHERLAND_ts <- ts(NEITHERLAND, start=c(2004,03), end=c(2019,05), frequency=12)
# Viewing and Checking the Created Time Series Data
NEITHERLAND_ts
sum(is.na(NEITHERLAND_ts))
library(forecast)
NEITHERLAND_ts <- tsclean(NEITHERLAND_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(NEITHERLAND_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
NEITHERLAND_ts_model <- auto.arima(NEITHERLAND_ts)
NEITHERLAND_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
NEITHERLAND_ts_forecast <- forecast (NEITHERLAND_ts_model, level=c(95), h=259)
plot(NEITHERLAND_ts_forecast)
NEITHERLAND_ts_forecast
write.table(NEITHERLAND_ts_forecast, file="Neitherland_TSA.csv", sep=",")
# Importing Data
SLOVAKIA <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Slovakia.xlsx")
# Creating Time Series Data
SLOVAKIA_ts <- ts(SLOVAKIA, start=c(2004,01), end=c(2019,06), frequency=12)
# Viewing and Checking the Created Time Series Data
SLOVAKIA_ts
sum(is.na(SLOVAKIA_ts))
library(forecast)
SLOVAKIA_ts <- tsclean(SLOVAKIA_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SLOVAKIA_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SLOVAKIA_ts_model <- auto.arima(SLOVAKIA_ts)
SLOVAKIA_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SLOVAKIA_ts_forecast <- forecast (SLOVAKIA_ts_model, level=c(95), h=258)
plot(SLOVAKIA_ts_forecast)
SLOVAKIA_ts_forecast
write.table(SLOVAKIA_ts_forecast, file="Slovakia_TSA.csv", sep=",")
# Importing Data
SPAIN <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Spain.xlsx")
# Creating Time Series Data
SPAIN_ts <- ts(SPAIN, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
SPAIN_ts
sum(is.na(SPAIN_ts))
library(forecast)
SPAIN_ts <- tsclean(SPAIN_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(SPAIN_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
SPAIN_ts_model <- auto.arima(SPAIN_ts)
SPAIN_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
SPAIN_ts_forecast <- forecast (SPAIN_ts_model, level=c(95), h=257)
plot(SPAIN_ts_forecast)
SPAIN_ts_forecast
write.table(SPAIN_ts_forecast, file="Spain_TSA.csv", sep=",")
# Importing Data
TURKEY <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/Turkey.xlsx")
# Creating Time Series Data
TURKEY_ts <- ts(TURKEY, start=c(2002,03), end=c(2019,04), frequency=12)
# Viewing and Checking the Created Time Series Data
TURKEY_ts
sum(is.na(TURKEY_ts))
library(forecast)
TURKEY_ts <- tsclean(TURKEY_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(TURKEY_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
TURKEY_ts_model <- auto.arima(TURKEY_ts)
TURKEY_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
TURKEY_ts_forecast <- forecast (TURKEY_ts_model, level=c(95), h=260)
plot(TURKEY_ts_forecast)
TURKEY_ts_forecast
write.table(TURKEY_ts_forecast, file="Turkey_TSA.csv", sep=",")
# Importing Data
UK <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/UK.xlsx")
# Creating Time Series Data
UK_ts <- ts(UK, start=c(2004,01), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
UK_ts
sum(is.na(UK_ts))
library(forecast)
UK_ts <- tsclean(UK_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(UK_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
UK_ts_model <- auto.arima(UK_ts)
UK_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
UK_ts_forecast <- forecast (UK_ts_model, level=c(95), h=257)
plot(UK_ts_forecast)
UK_ts_forecast
write.table(UK_ts_forecast, file="UK_TSA.csv", sep=",")
library(readxl)
France <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
View(France)
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,07), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=257)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,04), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=257)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,05), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=259)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,04), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=262)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
library(readxl)
France <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
View(France)
# Importing Data
FRANCE <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Cyprus/France.xlsx")
# Creating Time Series Data
FRANCE_ts <- ts(FRANCE, start=c(2004,1), end=c(2019,04), frequency=12)
# Viewing and Checking the Created Time Series Data
FRANCE_ts
sum(is.na(FRANCE_ts))
library(forecast)
FRANCE_ts <- tsclean(FRANCE_ts)
# Step – 1 of the Box-Jenkins Methodology (Identification: Plotting the Time Series Data)
plot(FRANCE_ts)
# Step-2 of the Box-Jenkins Methodology (Estimating the appropriate model)
FRANCE_ts_model <- auto.arima(FRANCE_ts)
FRANCE_ts_model
# Forecasting
options(max.print=1000000)
library(forecast)
FRANCE_ts_forecast <- forecast (FRANCE_ts_model, level=c(95), h=262)
plot(FRANCE_ts_forecast)
FRANCE_ts_forecast
